Nowcasting with Google, by Mahalia Jackman

Nowcasting has become widely popular in economics. In its most basic form, nowcasting can be summarised as ‘predicting the present and sometimes recent past’. At first this may seem a bit strange. Why would economists want to forecast the present, or even the past? Can you even forecast the past? Economists ‘nowcast’ because of the significant time lag in statistical releases. For instance, inflation statistics for July 2013 were released in October 2013; and don’t even get me started on the national accounts data. We often want to know what is going on in the economy in real time. For example, we want to know what happened in the tourism industry in May on June 1st. So economists began looking for high frequency indicators which tend to be highly correlated with the ‘late release’ data. The problem is, even the leading indicators have a lag…until Google!

Putting Google Data to Work

Recently, Google began to release real-time information on its users’ search queries though its Google Trends interface. The data is of a weekly frequency and dates back to 2004. You can filter the results for the search term by country (i.e. the location where the search was generated), time period and category (for instance, searches in the Google Travel category or Google Finance). This has proven to be a useful data source as it allows researchers to observe interest in a product, brand or society in general. In fact, in a recent paper by Google economists Hyunyoung Choi and Hal Varian titled “Predicting the present with Google trends” illustrated the usefulness of Google data for nowcasting a host of US economic time series, including automobile sales, unemployment claims and even trends in travel. Since then, several other researchers have explored the use of Google search data as an economic indicator. Google trends has been used to nowcast growth cycles in Israel, private consumption in the US, some indicators of labour and housing markets in the UK, unemployment in Turkey, retail and unemployment in Belgium, automobiles in Chile, and the list goes on.

Applying Google Econometrics to the Caribbean

After reading several studies on the larger economies, I started to think about the applicability in Caribbean. So I – with the aid of Simon Naitram – put Google econometrics to the test. We focused on whether or not observing internet habits in Barbados main tourism markets can provide insights on trends in tourist arrivals to Barbados[i].

Why tourism? First, Barbados’ economic fortunes are closely tied to its tourism industry and as recent economic activity highlights, if tourism sneezes, the entire country catches pneumonia! Second, tourism is often described as an information intensive industry. Quite a few studies have shown that the internet (particularly search engines) has become one of the most effective means for tourists to seek information on destinations or discounts on hotels and flights. We assumed that Google search queries may be capable of providing information on the level of interest in Barbados, and so serve as a leading indicator of tourism demand.

The Results

So…how predictable is tourism using this algorithm? Here is one of the examples from the paper. The blue line shows weekly Canadian arrivals and the red dotted line represents Canadian search volume histories related to the simple search term “Barbados” under the Travel category of Google Trends. Even through a simple visual examination there does seem to be a high degree of correlation, and the Google trends series even mirrors the seasonality in Canadian arrivals.

However, just because they are correlated does not mean that Google can ‘nowcast’ tourism. Forecasting 101 tells us that performance is relative: the predictive power of a model is only meaningful in relation to some baseline means of prediction. So, we proceeded to nowcast Canadian arrivals based on its past values (this is a simplification of the process- to be specific, we used an autoregressive support vector regression that you can read more about in our paper). We then compared them with a model incorporating the Google data (which we deemed the Google-based support vector regression). In a nutshell, if the model with Google provides better predictions, then we can conclude that Google search query volumes add insight on tourism behaviour.

The figure below plots the two nowcasts as well as the actual data over a 12-week period. The red line shows the nowcast of arrivals from Canada with Google, the blue line shows the nowcasts without Google and the grey bars represent the actual data.

A key finding from the paper was that the accuracy of Google predictability varies. For some tourist markets (like Canada) Google trends worked very well, while for others (like the US), Google data did not provide any additional information from what can be “learned” from past behaviour.

Google Data Pros and Cons

It should be noted that Google data is a mixed blessing. On the positive side, Google trends data have a large number of characteristics that are appealing; for instance, they contain information on a large portion of internet users, the information is free and it is practically available in real time. On the other hand, it has a number of shortcomings:

Google trends uses relative data rather than absolute. That is, it doesn’t give the total number of searches, but an index which is relative to all search queries in a particular region.

Different users interested in the same topic could enter entirely different search queries & different users with entirely different intentions could enter very similar search queries (though this is not likely to impact what Simon and I did, as we limited the results to only searches in the ‘Travel’ category)

Internet searches are correlated with factors such as age, income, etc., which may limit predictability in certain market types

Currently, Google can only generate search queries originating from four Caribbean countries – the Dominican Republic, Haiti, Jamaica and Trinidad.

What can we take from this?

Google Econometrics has the potential to act as a leading indicator

Predictability depends – so be sure to test the nowcasts against some benchmark model

Beware of the short-comings and use wisely!

Mahalia Jackman is Head of Model Development at Antilles Economics. She can be contacted at *protected email* or via her linkedin page.